基于临床依据的分层分类用于一致性胸部X射线影像解读
Clinically-Grounded Hierarchical Classification for Consistent Chest X-ray Interpretation
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中文总结 AI 辅助
本研究针对胸部X射线解读的层级特性，提出CHASE框架，通过三级分类与多层级优化，提升了预测的一致性与性能。
中文摘要 AI 辅助
准确的胸部X射线影像解读本质上是分层的。临床决策不仅取决于存在何种异常，还取决于其所在位置，需要从广泛的解剖系统到特定病理发现进行推理。然而，现有自动化系统大多将此视为平面分类问题，无法捕捉层级间依赖关系或强制粗粒度与细粒度预测之间的一致性。我们提出CHASE（Classification with Hierarchical Analysis and Structured Enforcement，即分层分析与结构化约束分类），这是一个统一的单阶段框架，通过临床驱动的三级分类体系模拟放射科医生从粗到细的推理，该体系包含9个解剖区域、17个子区域和28种病理发现。CHASE在共享的Vision Transformer骨干网络中联合优化多层级监督、跨层级概率对齐和层级违规惩罚，确保细粒度病理发现由其粗粒度层级的解剖上下文支持，而非孤立预测。实验表明，CHASE在所有层级上均优于平面和分层基线，同时实现了更优的概率层级一致性，层级注意力图也证实了其基于解剖的预测。代码可在该URL获取。
英文摘要
Accurate chest X-ray interpretation is inherently hierarchical. Clinical decisions depend not only on what abnormality is present but where it is situated, requiring reasoning from broad anatomical systems down to specific pathological findings. Yet existing automated systems largely treat this as a flat classification problem, failing to capture inter-level dependencies or enforce coherence between coarse and fine predictions. We propose CHASE (Classification with Hierarchical Analysis and Structured Enforcement), a unified single-stage framework that mirrors radiologists' coarse-to-fine reasoning through a clinically driven three-level taxonomy of 9 anatomical regions, 17 sub-regions, and 28 pathological findings. CHASE jointly optimizes multi-level supervision, cross-level probability alignment, and a hierarchy-violation penalty within a shared Vision Transformer backbone. This ensures that fine-grained findings are anatomically supported by their coarser-level context rather than predicted in isolation. Experiments demonstrate that CHASE outperforms flat and hierarchical baselines across all levels while achieving superior probabilistic hierarchy consistency, with level-wise attention maps confirming anatomically grounded predictions. Code is available at: https://github.com/yejix-ai/CHASE.
发表机构
- Yeji X（艺智X）
机构由 AI 辅助整理，请以论文原文为准。
